'B-FASST': A Statistical and Spatial Analysis System to Evaluate Player Performance in Soccer

Abstract

Despite soccer being the number one sport in the world in many respects, the “beautiful game” still lags behind other sports in terms of analytics. We propose the FASST system (Football Analytics: Spatial and Statistical) as an instrument to evaluate player performance. In the basic FASST (BFASST) system, a plus/minus-metric subtracts goals conceded from goals scored for each player while on the pitch. Key improvements to the established hockey plus/minus-metric include control for opponents’ strength and for the importance of a goal helping to identify key players. While the plus/minus-metric identifies key players, the spatial portion provides reasons for the players’ performances adding to established spatial mapping techniques. This allows for valid comparisons of a team’s performance when a player was on/off the pitch. The dependent variables evaluating a player’s performance in B-FASST’s spatial analytics are team shots for/against. Maps were created representing the difference between when a player was on/off the pitch which identifies differences in the distribution of shots for/against under different scenarios. When the components of B-FASST work in concert, one can test specific hypotheses. As an example, we show Franck Ribery’s (Bayern Munich) performance depends strongly on who is playing left back behind him.